Point cloud data is utilized in a variety of applications, and point cloud data is especially important in many spatial perception applications such as augmented reality, scene visualization, and robot navigation. Point clouds are typically represented as a data structure that includes a set of points (e.g., each point may be stored as a vector of three coordinates related to a coordinate system in three-dimensional space). Conventional point processing techniques may be associated with demanding computational power and memory requirements due to the large number of points stored in a given point cloud. For example, a conventional depth camera may generate 100 k raw data points in the point cloud per frame.
In order to facilitate the processing of a large number of points stored in the point cloud, data compression techniques may be used. Techniques may include the separation of points into a spatial subdivision hierarchy such as an octree. Spatial decomposition of the point cloud data can be used to sub-sample the geometry represented by the point cloud by exploiting spatial coherency, but the discretization of 3D space may produce artifacts. Thus, there is a need for addressing these issues and/or other issues associated with the prior art.